Structure–Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

Drug design based on kinetic properties is growing in
application.
Here, we applied retrosynthesis-based pre-trained molecular representation
(RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins
and successfully predicted the dissociation rate constant (koff) values of 38 inhibitors from an independent
dataset for the N-terminal domain of heat shock protein 90α
(N-HSP90). Our RPM molecular representation outperforms other pre-trained
molecular representations such as GEM, MPG, and general molecular
descriptors from RDKit. Furthermore, we optimized the accelerated
molecular dynamics to calculate the relative retention time (RT) for
the 128 inhibitors of N-HSP90 and obtained the protein–ligand
interaction fingerprints (IFPs) on their dissociation pathways and
their influencing weights on the koff value.
We observed a high correlation among the simulated, predicted, and
experimental −log(koff) values.
Combining ML, molecular dynamics (MD) simulation, and IFPs derived
from accelerated MD helps design a drug for specific kinetic properties
and selectivity profiles to the target of interest. To further validate
our koff predictive ML model, we tested
our model on two new N-HSP90 inhibitors, which have experimental koff values
and are not in our ML training dataset. The predicted koff values are consistent with experimental data, and
the mechanism of their kinetic properties can be explained by IFPs,
which shed light on the nature of their selectivity against N-HSP90
protein. We believe that the ML model described here is transferable
to predict koff of other proteins and
will enhance the kinetics-based drug design endeavor.